AI Coding Isn't Too Expensive — Nobody's Measured What It's Worth
Two stories have dominated enterprise AI circles these past couple of weeks: Microsoft quietly killing Claude Code inside its Experiences + Devices division and herding thousands of engineers back onto GitHub Copilot; and Uber burning through its entire 2026 AI coding tool budget in just four months.
The consensus take is almost unanimous: AI coding is too expensive, the bubble is cracking. Heavy users staring down monthly token bills of $500 to $2,000 — yeah, that’s a number that’ll get anyone’s attention.
I think that reading is wrong. What’s actually getting reckoned with here isn’t “AI is too expensive.” It’s something far more embarrassing: almost nobody measured what any of this money was buying.
One Detail That Gives the Game Away
The most revealing thing buried in Uber’s story is this: they launched an internal leaderboard ranking teams by AI tool usage. By March, 84% of their 5,000 engineers had been classified as “agentic coding users.”
Stop and think about what that leaderboard was actually incentivizing — it was rewarding token consumption, not value delivered. When you turn “used it the most” into a public badge of honor, of course people are going to burn through tokens. The blown budget wasn’t a surprise. It was the mathematically inevitable outcome of that incentive structure.
So when the bills arrived, finance could see a cost figure accurate to the dollar — and benefits that couldn’t be articulated at all. Uber’s own COO was admirably blunt about it: the line between dollars spent and features shipped “just doesn’t connect yet… it’s hard to say we’re producing 25% more useful features right now.”
That’s not an AI failure. That’s a measurement failure.
You Can’t Win a Budget Fight With “It Feels Faster”
Here’s the real irony: these same companies are the ones furiously building evaluation frameworks for their AI products — gold-standard datasets, quality benchmarks, every tenth of a point quantified. But for the AI tools their own engineers use, “productivity gains” became an unexamined default assumption, never a hypothesis that needed proving.
“Faster” was treated as self-evident. Nobody connected agent hours to actual shipped work, to actual delivered value. The result: when the CFO walks in holding a $5 million invoice and asks “what did we get for this?”, engineering can only say “it felt a lot faster” — and “felt” in a budget meeting is worth approximately nothing.
What This Reckoning Is Actually About
This isn’t a reckoning for AI coding. It’s a reckoning for treating AI as performance rather than leverage.
Rolling out tools, publishing a usage leaderboard, hitting 84% penetration — that’s all the appearance of adoption, not a strategy. A real strategy means deciding upfront what you’re trying to move, then measuring whether it moved, then tracing that movement back to value.
My read: the teams that survive this aren’t the ones that cut AI tools the hardest or use them the most sparingly. They’re the ones who can connect token spend to shipped value and take that line into a room with a CFO. This cost correction is going to cleanly separate teams that use AI as leverage from teams that used AI as theater.
One more uncomfortable point: if you’ve spent the past year measuring usage, you’ve already lost this argument — because you trained your entire organization to optimize the dashboard, not the value.
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